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Robust Forecasting

Author

Listed:
  • Timothy Christensen

    (New York University)

  • Hyungsik Roger Moon

    (Univ. of Southern California Schae?er Center, and Yonsei University)

  • Frank Schorfheide

    (University of Pennsylvania CEPR, NBER, and PIER)

Abstract

We use a decision-theoretic framework to study the problem of forecasting discrete outcomes when the forecaster is unable to discriminate among a set of plausible forecast distributions because of partial identi?cation or concerns about model misspeci?cation or structural breaks. We derive “robust” forecasts which minimize maximum risk or regret over the set of forecast distributions. We show that for a large class of models including semiparametric panel data models for dynamic discrete choice, the robust forecasts depend in a natural way on a small number of convex optimization problems which can be simpli?ed using duality methods. Finally, we derive “e?cient robust” forecasts to deal with the problem of ?rst having to estimate the set of forecast distributions and develop a suitable asymptotic e?ciency theory.

Suggested Citation

  • Timothy Christensen & Hyungsik Roger Moon & Frank Schorfheide, 2020. "Robust Forecasting," PIER Working Paper Archive 20-038, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    • Handle: RePEc:pen:papers:20-038
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    File URL: https://economics.sas.upenn.edu/sites/default/files/filevault/20-038.pdf
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    Other versions of this item:

    • Timothy Christensen & Hyungsik Roger Moon & Frank Schorfheide, 2020. "Robust Forecasting," Papers 2011.03153, arXiv.org, revised Dec 2020.

    More about this item

    Keywords

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    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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